



'''simple docstring'''






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

lowerCAmelCase:   Optional[Any]					=		logging.get_logger(__name__)

@add_end_docstrings(lowerCamelCase__      )
class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    str		,     *__snake_case					:    int		,     **__snake_case					:    List[str]      ):
      super().__init__(*__snake_case		,     **__snake_case      )
      requires_backends(self		,     'vision'      )
      self.check_model_type(__snake_case      )







    def __call__( self					:    List[Any]		,     __snake_case					:    Union[str, List[str], "Image.Image", List["Image.Image"]]		,     **__snake_case					:    List[Any]      ):
      return super().__call__(__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     **__snake_case					:    Optional[int]      ):
      return {}, {}, {}







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Dict      ):
      a		:   Optional[Any]	  =	load_image(__snake_case      )
      a		:   Any	  =	image.size
      a		:   Optional[Any]	  =	self.image_processor(images=__snake_case		,     return_tensors=self.framework      )
      return model_inputs







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[str]      ):
      a		:   List[Any]	  =	self.model(**__snake_case      )
      return model_outputs







    def      lowercase_			( self					:    str		,     __snake_case					:    int      ):
      a		:   Optional[Any]	  =	model_outputs.predicted_depth
      a		:   int	  =	torch.nn.functional.interpolate(
          predicted_depth.unsqueeze(1      )		,     size=self.image_size[::-1]		,     mode='bicubic'		,     align_corners=__snake_case      )
      a		:   Tuple	  =	prediction.squeeze().cpu().numpy()
      a		:   Tuple	  =	(output * 2_55 / np.max(__snake_case      )).astype('uint8'      )
      a		:   Dict	  =	Image.fromarray(__snake_case      )
      a		:   Any	  =	{}
      a		:   Dict	  =	predicted_depth
      a		:   int	  =	depth
      return output_dict




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )